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WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild

文献类型：期刊论文

作者Zhang, Shifeng2,3; Xie, Yiliang4; Wan, Jun2,3; Xia, Hansheng5; Li, Stan Z.2,3,6; Guo, Guodong1,7
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2020-02-01
卷号22期号:2页码:380-393
ISSN号1520-9210
关键词Benchmark testing Detectors Training Urban areas Cameras Task analysis Deep learning Pedestrian detection dataset rich diversity high density
DOI10.1109/TMM.2019.2929005
通讯作者Wan, Jun(jun.wan@nlpr.ia.ac.cn)
英文摘要Pedestrian detection has achieved significant progress with the availability of existing benchmark datasets. However, there is a gap in the diversity and density between real world requirements and current pedestrian detection benchmarks: first, most existing datasets are taken from a vehicle driving through the regular traffic scenario, usually leading to insufficient diversity; second, crowd scenarios with highly occluded pedestrians are still underrepresented, resulting in low density. To narrow this gap and facilitate future pedestrian detection research, we introduce a large and diverse dataset named WiderPerson for dense pedestrian detection in the wild. This dataset involves five types of annotations in a wide range of scenarios, no longer limited to the traffic scenario. There are a total of 13 382 images with 399 786 annotations, that is, 29.87 annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions. Hence, pedestrians in the proposed dataset are extremely challenging due to large variations in the scenario and occlusion, which is suitable to evaluate pedestrian detectors in the wild. We introduce an improved Faster R-CNN and the vanilla RetinaNet to serve as baselines for the new pedestrian detection benchmark. Several experiments are conducted on previous datasets including Caltech-USA and CityPersons to analyze the generalization capabilities of the proposed dataset, and we achieve state-of-the-art performances on these previous datasets without bells and whistles. Finally, we analyze common failure cases and find the classification ability of pedestrian detector needs to be improved to reduce false alarm and misdetection rates. The proposed dataset is available at http://www.cbsr.ia.ac.cn/users/sfzhang/WiderPerson.
WOS关键词MULTIPLE
资助项目National Key Research and Development Plan[2016YFC0801002] ; Chinese National Natural Science Foundation[61876179] ; Chinese National Natural Science Foundation[61872367] ; Chinese National Natural Science Foundation[61806203] ; Science and Technology Development Fund of Macau[152/2017/A] ; Science and Technology Development Fund of Macau[0025/2018/A1] ; Science and Technology Development Fund of Macau[008/2019/A1]
WOS研究方向Computer Science ; Telecommunications
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000510676300008
资助机构National Key Research and Development Plan ; Chinese National Natural Science Foundation ; Science and Technology Development Fund of Macau
源URL[http://ir.ia.ac.cn/handle/173211/38469]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Wan, Jun
作者单位1.Baidu Res, Inst Deep Learning, Beijing 100193, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Biometr Secur Res, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
4.Univ Southern Calif, Los Angeles, CA 90007 USA
5.Nanjing Univ Aeronaut & Astronaut, Coll Energy & Power Engn, Nanjing 210016, Peoples R China
6.Macau Univ Sci & Technol, Macau 999078, Peoples R China
7.Natl Engn Lab Deep Learning Technol & Applicat, Beijing 100193, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Shifeng,Xie, Yiliang,Wan, Jun,et al. WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2020,22(2):380-393.
APA Zhang, Shifeng,Xie, Yiliang,Wan, Jun,Xia, Hansheng,Li, Stan Z.,&Guo, Guodong.(2020).WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild.IEEE TRANSACTIONS ON MULTIMEDIA,22(2),380-393.
MLA Zhang, Shifeng,et al."WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild".IEEE TRANSACTIONS ON MULTIMEDIA 22.2(2020):380-393.

入库方式： OAI收割

来源：自动化研究所

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